3D Monitoring Model for Real‐Time Displacement of Metro Tunnel under “Dual Carbon” Background
Bibliographic record
Abstract
Real‐time automatic displacement monitoring of metro tunnels is vital for ensuring operational safety and contributes to carbon reduction goals by improving system efficiency. This study focuses on key monitoring elements such as displacement, settlement, convergence, and cracking. Through the analysis of continuous monitoring data, a real‐time displacement monitoring model for metro tunnels based on robotic total stations is proposed. This model can timely identify potential risks, thereby ensuring the safe operation of tunnels and reducing carbon emissions from unnecessary maintenance operations, thereby reducing the carbon footprint of metro operations. This article takes the Jinan Metro Tunnel Displacement Real‐time Monitoring Project in China as a case study and constructs a comprehensive monitoring framework using robotic total stations, intelligent automated deformation monitoring data collectors, and cloud servers. The implementation details of the project, displacement monitoring principles, monitoring system construction, and data analysis processes are elaborated in detail. Taking the monitoring data of Jinan Metro Line 2 from April 1, 2022, to May 31, 2023, as an example, the results show that the tunnel displacement is within the safe range, verifying the practical application value of the method proposed in this paper. It can effectively ensure the safe operation of the metro and promote sustainable development and low‐carbon metro construction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".